23. Iain Harlow - Leaving academia for industry and optimizing how you learn
Towards Data Science
03/03/20
•45m
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I can’t remember how many times I’ve forgotten something important.
I’m sure it’s a regular occurrence though: I constantly forget valuable life lessons, technical concepts and useful bits of statistical theory. What’s worse, I often forget these things after working bloody hard to learn them, so my forgetfulness is just a giant waste of time and energy.
That’s why I jumped at the chance to chat with Iain Harlow, VP of Science at Cerego — a company that helps businesses build training courses for their employees by optimizing the way information is served to maximize retention and learning outcomes.
Iain knows a lot about learning and has some great insights to share about how you can optimize your own learning, but he’s also got a lot of expertise solving data science problems and hiring data scientists — two things that he focuses on in his work at Cerego. He’s also a veteran of the academic world, and has some interesting observations to share about the difference between research in academia and research in industry.
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22. Luke Marsden - Data Science Infrastructure and MLOps
February 23, 2020
•40m
You train your model. You check its performance with a validation set. You tweak its hyperparameters, engineer some features and repeat. Finally, you try it out on a test set, and it works great!
Problem solved? Well, probably not.
Five years ago, your job as a data scientist might have ended here, but increasingly, the data science life cycle is expanding to include the steps after basic testing. This shouldn’t come as a surprise: now that machine learning models are being used for life-or-death and mission-critical applications, there’s growing pressure on data scientists and machine learning engineers to ensure that effects like feature drift are addressed reliably, that data science experiments are replicable, and that data infrastructure is reliable.
This episode’s guest is Luke Marsden, and he’s made these problems the focus of this work. Luke is the founder and CEO of Dotscience, a data infrastructure startup that’s creating a git-like tool for data science version control. Luke has spent most of his professional life working on infrastructure problems at scale, and has a lot to say about the direction data science and MLOps are heading in.
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Most machine learning models are used in roughly the same way: they take a complex, high-dimensional input (like a data table, an image, or a body of text) and return something very simple (a classification or regression output, or a set of cluster centroids). That makes machine learning ideal for automating repetitive tasks that might historically have been carried out only by humans.
But this strategy may not be the most exciting application of machine learning in the future: increasingly, researchers and even industry players are experimenting with generative models, that produce much more complex outputs like images and text from scratch. These models are effectively carrying out a creative process — and mastering that process hugely widens the scope of what can be accomplished by machines.
My guest today is Xander Steenbrugge, and his focus is on the creative side of machine learning. In addition to consulting with large companies to help them put state-of-the-art machine learning models into production, he’s focused a lot of his work on more philosophical and interdisciplinary questions — including the interaction between art and machine learning. For that reason, our conversation went in an unusually philosophical direction, covering everything from the structure of language, to what makes natural language comprehension more challenging than computer vision, to the emergence of artificial general intelligence, and how all these things connect to the current state of the art in machine learning.
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